Different Depth Binning.    When implementing plane-sweep stereo [1]}, [2]} to construct the cost volume, we need to sample \(M_0\) =64 plane hypotheses. In our main experiments, we use the inverse depth bins, i.e., the plane hypotheses is uniformly sampled in the inverse depth space, s.t. \( 1/d \sim U(d_{\text{min}}, d_{\text{max}})\) . Here we set \(d_{min}\) =0.25 and \(d_{max}\) =20 meters for indoor scenes (e.g., ScanNet [3]}). We also test linear depth bins, i.e., \(d \sim U(d_{\text{min}}, d_{\text{max}})\) , and hand-crafted depth bins by calculating the depth distribution on ScanNet. But we found the inverse depth binning achieves the best results, as we reported in the main paper. We also test adaptive depth bins as in [4]}, where the depth bins are dynamically generated conditioned on the global feature learned by a transformer layer. For our(+pose) variant, adaptive depth bins lead to marginal improvement than the inverse depth bins. However, for our(+pose,atten) variant, adaptive depth bins hinder the depth accuracy.
